Liu Jingxiao, Li Haipeng, Noh Hae Young, Santi Paolo, Biondi Biondo, Ratti Carlo
Senseable City Laboratory, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA.
Department of Geophysics, Stanford University, Stanford, CA, 94305, USA.
Nat Commun. 2025 Mar 31;16(1):3091. doi: 10.1038/s41467-025-57997-y.
The analysis of urban seismic signals offers valuable insights into urban environments and society. Yet, accurate detection and localization of seismic sources on a city-wide scale with conventional seismographic network is unavailable due to the prohibitive costs of ultra-dense seismic arrays required for imaging high-frequency anthropogenic sources. Here, we leverage existing fiber-optic networks as a distributed acoustic sensing system to accurately locate urban seismic sources and estimate how their intensity varies over time. By repurposing a 50-kilometer telecommunication fiber into an ultra-dense seismic array, we generate spatiotemporal maps of seismic source power (SSP) across San Jose, California. Our approach overcomes the proximity limitations of urban seismic sensing, enabling accurate localization of remote seismic sources generated by urban activities, such as traffic, construction, and school operations. We also show strong correlations between SSP values and environmental noise levels, as well as various persistent urban features, including land use patterns and demographics.
对城市地震信号的分析为了解城市环境和社会提供了有价值的见解。然而，使用传统地震台网在全市范围内准确检测和定位地震源是不可行的，因为成像高频人为源所需的超密集地震阵列成本过高。在此，我们利用现有的光纤网络作为分布式声学传感系统，以准确地定位城市地震源，并估计其强度随时间的变化情况。通过将一条50公里长的通信光纤重新用作超密集地震阵列，我们生成了加利福尼亚州圣何塞市地震源功率（SSP）的时空图。我们的方法克服了城市地震传感的近距离限制，能够准确地定位由城市活动（如交通、建筑和学校运营）产生的远程地震源。我们还展示了SSP值与环境噪声水平以及各种持久的城市特征（包括土地利用模式和人口统计数据）之间的强相关性。
